University Research Project Analyzes Bitcoin Blockchain to Combat Sex Trade

Researchers from several American universities are using artificial intelligence to examine the bitcoin blockchain to identify victims of sexual exploitation. The initiative plans to develop a freely available suite of tools that can assist authorities in tracking and identifying individuals working in the underground sex industry.

The Project Will Analyze Online Ads and the Bitcoin Blockchain to Identify Participants in Illegal Sex Trade

Researchers from the New York University Tandon School of Engineering, the University of California in Berkley, and the University of California in San Diego have teamed up to develop software designed to combat the sex trade through examining the bitcoin blockchain. The project seeks to develop machine-learning algorithms that can scan and identify patterns in sex ads, including the identification of illegal prostitution rings by identifying cryptocurrency wallets that are associated with multiple ads.

UC Berkeley doctoral candidate, Rebecca Portnoff, has had significant involvement in the bitcoin blockchain analysis project.” Portnoff stated that “the technology we’ve built finds connections between ads. Is the pimp behind that post for Backpage also behind this post in Craigslist? Is he the same man who keeps receiving Bitcoin for trafficked girls? Questions like these are answerable only through more sophisticated technological tools – exactly what we’ve built in this work – that link ads together using payment mechanisms and the language in the ads themselves. Portnoff also stated “where previously you might have five different phone numbers that you had no idea were connected, when you can see that they all came from the same wallets, that the same person paid for them, that’s a concrete sign that these five phone numbers are all related to each other.”

The Projects Hopes to Deliver “A Big Boost for Those Working to Curb Exploitation”

The project will predominantly target ads hosted on websites such as Backpage and Craiglist, where human traffickers are known to operate. Until now, law enforcement have struggled to identify the ring leaders of illegal prostitution rings operating online due to the pseudonymous nature of online ads, culprits’ use of multiple phone numbers, email accounts and aliases, and the challenge of ascertaining whether or not an ad has been posted by a victim of the sex trade or a willing sex worker.

Damon McCoy, an NYU assistant professor of computer science and co-author of the research believes that the project will have a significant impact on the illegal sex industry, stating that “There are hundreds of thousands of these ads placed every year, and any technique that can surface commonalities between ads and potentially shed light on the owners is a big boost for those working to curb exploitation.”

The development of the online ad and bitcoin blockchain analysis algorithms has been funded with assistance from the Amazon Web Services Cloud Credits for Research Program, the technology and security firm Giant Oak, Google, the National Science Foundation and the U.S. Department of Education. UC Berkeley has stated that the project’s findings will be published by Association for Computing Machinery’s Conference on Knowledge Discovery and Data Mining.

Do you think that the joint university project will be able to identify underground prostitution rings through bitcoin blockchain and online ad analysis? Share your thoughts in the comments section below!

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Blockchain analysis is big business. The U.S. government alone has spent $6 million on transaction mapping tools, while cryptocurrency exchanges… read more.

Samuel Haig

Samuel Haig is a journalist who has been completely obsessed with bitcoin and cryptocurrency since 2012. Samuel lives in Tasmania, Australia, where he attended the University of Tasmania and majored in Political Science, and Journalism, Media & Communications. Samuel has written about the dialectics of decentralization, and is also a musician and kangaroo riding enthusiast.

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